While the reconstruction of images is optimized usually with pixel level loss, we can also consider high level properties of the data. This kind of measure, called perceptual loss [1]}, compares the output of the reconstruction with the original image at high level neurons (presented near the end of the network). Here we consider the perceptual loss using the mean and variance layers from the encoder network, enforcing the reconstruction to have the same statistical properties of the original input.
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